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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluation of Marine Boundary Layer Clouds Over the Northeast Pacific During the CSET Campaign in E3SM Version 2

It is still challenging to reproduce marine boundary layer (MBL) clouds well in large-scale models despite their importance to the Earth's radiation budget and hydrological cycle. This study evaluates representation of the MBL and clouds in the Energy Exascale Earth System Model (E3SM) version 2. This study compares the E3SM simulation results with remote sensing and reanalysis data during the Cloud System Evolution in the Trades (CSET) field campaign to better understand the stratocumulus-to-cumulus cloud transition (SCT) over the northeast Pacific. E3SM results are extracted along the CSET Lagrangian trajectories. The comparison shows that the E3SM simulation applying horizontal wind nudging performs well in reproducing thermodynamic variables of the MBL and evolution trends of cloud variables along the trajectories. However, substantial overestimations of aerosol and cloud drop number (N d ) are observed, which is explained as an issue with version 2 of the model. Cloud fraction (CF) does decrease from the Californian coast to Hawaii in the E3SM simulation, but most CF values indicate either an overcast or almost clear sky, which differs from satellite and reanalysis data. The effect of N d overestimation on CF evolution is assessed via prescribed-N d simulations. Those simulations with N d modifications show negligible CF changes. A comparison of estimated inversion strength (EIS) also shows that the simulated EIS values are similar to those of reanalysis data. Our study suggests that cloud macrophysics and boundary layer processes are more important in improving the simulation to capture the SCT than refining the model's thermodynamics or cloud microphysics.

Choi, Kyoung Ock [Univ. of Washington, Seattle, WA↗
DOE OSTI2025

Monitoring Fracture Hydromechanical Evolution in the Lab and Field Using Unsupervised Metric Learning

Fractures evolve in time through thermal‐hydraulic‐mechanical‐chemical (THMC) processes that alter their long‐range hydraulic transport properties and modify subsurface behavior and activities. The location of subsurface fractures makes it necessary to use remote sensing techniques such as passive or active seismic monitoring for fracture characterization. In this paper, we develop a machine learning approach to monitor the evolution of fracture properties using passive seismic sources in a laboratory setting and using active seismic monitoring from the Sanford Underground Research Facility in Lead, South Dakota, at a depth of 1.25 km in amphibolite rock during stimulation of natural fractures as well as during induced fracturing. The unsupervised metric learning technique applies tandem neural networks (twin (Siamese) or triplet) with contrastive loss and adaptive margins to track slowly varying systems for which class or similarity labels are not available. The approach adopts locality‐sensitive hashing to divide time‐ordered contiguous data into an arbitrary number of pseudo‐classes. Contrastive‐loss training with many hash bins generates an evolving latent‐space trajectory. This approach enables unsupervised metric learning for seismic data stacks under the condition of contiguous state sampling and slowly varying fracture properties. The displacement discontinuity theory provides a mechanistic foundation for the fracture‐dependent trajectories that are related to relaxation of fractures with time‐dependent specific stiffness responding to changes in stress or fluid saturation.

02 PETROLEUM↗
DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins

Abstract Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

Mathematical & Computational Biology↗
DOE OSTI2026

Kinetic-controlled transformations of group-III arsenide nanocubes

Tracking the structural evolution of colloidal nanocrystals (NCs) facilitates the mechanistic studies of their materials chemistry. NC engineering via phase transformation reveals the chemical and physical determinants that drive lattice-scale dynamic processes such as cation exchange. Here, in this study, we employed NCs to demonstrate the cation exchange process from Cu 3 As to InAs and GaAs within nanocubes. The symmetry conversion in unit cells from cubic Cu 3 As to hexagonal InAs and GaAs can be described using a schematic cellular automaton model, which suggests a simplified cube-to-sphere transition. The strong covalent characteristics of III–V materials highlight the kinetic control that navigates the tailorable transformation through either an isotropic trajectory, leading to hollow structures, or a topotaxial trajectory, with abundant stacking faults. The reconstruction of complex covalent bonds is envisioned as the foundation for the synthesis of NCs.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Trends and 2025 Insights on the Rise of Electric Vehicles in the USA

Plug-in electric vehicles (EVs) are reshaping the transportation energy landscape, providing a practical alternative to petroleum fuels for a growing number of applications. EV sales grew 55x in the past decade (2014-2024) and 6x since 2020, driven by technological progress enabled by policies to reduce transportation emissions as well as industrial plans motivated by strategic value of EVs for global competitiveness, jobs and geopolitics. In 2024, 22% of passenger cars sold globally were EVs and opportunities for EVs beyond on-road applications are growing, including solutions to electrify off-road vehicles, maritime and aviation. This Review updates and expands our 2020 assessment of the scientific literature and describes the current status and future projections of EV markets, charging infrastructures, vehicle-grid integration and supply chains in the USA. EV is the lowest-emission motorized on-road transportation option, with life-cycle emissions decreasing as electricity emissions continue to decrease. Charging infrastructure grew in line with EV adoption but providing ubiquitous reliable and convenient charging remains a challenge. EVs are reducing electricity costs in several US markets and coordinated EV charging can improve grid resilience and reduce electricity costs for all consumers. The current trajectory of technology improvement and industrial investments points to continued acceleration of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Aging matrix visualizes complexity of battery aging across hundreds of cycling protocols

To reliably deploy lithium-ion batteries, a fundamental understanding of cycling aging behavior is critical. Battery aging consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 359 high energy density commercial Li(Ni,Co,Al)O 2 /graphite + SiO x cylindrical 21 700 cells cycled across 207 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory metrics. We develop a framework using interpretable machine learning and explainable features to generate an aging matrix that visually deconvolutes the complex battery degradation behavior. This generalizable data-driven mechanistic framework simplifies the complex interplay between cycling conditions, degradation modes, and SOH, acting as a hypothesis-generation tool to aid battery users in identifying key degradation regimes for further study and experimentation.

25 ENERGY STORAGE↗
DOE OSTI2024

Analytical model for the motion and interaction of two-dimensional active nematic defects

Here, we develop an approximate, analytical model for the velocity of defects in active nematics by combining recent results for the velocity of topological defects in nematic liquid crystals with the flow field generated from individual defects in active nematics. Importantly, our model takes into account the long-range interactions between defects that result from the flows they produce as well as the orientational coupling between defects inherent in nematics. Our work complements previous studies of active nematic defect motion by introducing a linear approximation that allows us to treat defect interactions as two-body interactions and incorporates the hydrodynamic screening length as a tuning parameter. We show that the model can analytically predict bound states between two +1/2 winding number defects, effective attraction between two –1/2 defects, and the scaling of a critical unbinding length between ±1/2 defects with activity. The model also gives predictions for the trajectories of defects, such as the scattering of +1/2 defects by –1/2 defects at a critical impact parameter that depends on activity. In the presence of circular confinement, the model predicts a braiding motion for three +1/2 defects that was recently seen in experiments, as well as stable and ergodic trajectories for four or more defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2026

The sweeper spectrometer for neutron invariant-mass spectroscopy at FRIB

Neutron invariant-mass spectroscopy (NIMS) is a key technique for studying unbound and weakly bound nuclei at the limits of stability. At the Facility for Rare Isotope Beams (FRIB), such measurements are performed using the Sweeper spectrometer, a large-gap, high-rigidity dipole system coupled to the MoNA-LISA neutron detector arrays. To meet the demands imposed by higher beam energies (>130 MeV/u) and the broad cocktail-beam selection available at FRIB, the spectrometer has recently been upgraded to improve particle-identification and detection performance. Upstream of the reaction target, a plastic scintillator with Silicon photomultiplier (SiPM) readout provides the global trigger and time reference, two parallel plate avalanche counters (PPACs) track the trajectories of incoming beam particles, and a silicon PIN detector measures the energy loss, ΔE, for charge (Z) identification. After the Sweeper magnet, the trajectories of the reaction products are tracked by two micro-pattern drift chambers (MPDCs), their charge (Z) is identified by a Frisch-grid ionization chamber (FG-IC), and their mass-to-charge ratio (A/Q) is deduced by time-of-flight measurement using a fast plastic scintillator read out by an array of photomultiplier tubes (PMTs). The detection system also incorporates the Modular Neutron Array (MoNA) for neutron detection and the CAESium-iodide scintillator ARray (CAESAR) for high-efficiency γ-ray measurements to enable full kinematic reconstruction. Performance was evaluated using a cocktail beam around 37 Al accelerated at E ≈ 130 MeV/u during the first FRIB campaign, demonstrating the readiness of the upgraded system for future studies of nuclei at and beyond the neutron drip line.

Particle identification methods↗
DOE OSTI2026

Accounting for linkages between wildfire-driven shifts in plant-microbial interactions and soil carbon dynamics in Arctic tundra

Increasing wildfire regimes in the rapidly changing Arctic tundra are altering the soil carbon budget through increased permafrost degradation, shrubs expansion, and shifts in microbial activities. Whether future arctic wildfires will result in net C losses or gains in the future will depend on complex biotic and abiotic interactions that regulate belowground C biogeochemical processes, including linkages among biotic communities. One important linkage is plant-microbe interactions. While these interactions are likely shaped or altered by wildfires, they remain little explored in the context of successional trajectories. Yet, incorporating plant-microbe interactions in frameworks for defining and understanding post-fire soil C trajectories is critical to predict belowground C responses to future tundra wildfires. Here, we provide examples of and discuss how fire-mediated changes in plant-soil-microbe (PSM) interactions can impact soil C dynamics in the Arctic tundra. We consider different impacts of wildfires on PSM interactions and their implications to soil C dynamics, as well as the nuances associated with particular wildfire regimes (severity and intensity) and successional timescales. We suggest that accounting for plant-microbial linkages in future wildfire-succession interactions frameworks can inform future experimental designs and reduce uncertainties in our ability to predict the net effect of Arctic wildfires on ecosystem C.

fungi, bacteria↗
DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗
DOE OSTI2026

Nonadiabatic strong-field photoionization revisited: Detailed analysis

We develop a theoretical framework to interpret “bump-like” enhancements in the energy distribution of electrons emitted from cesium atoms subjected to intense infrared laser fields, as observed in a recent experiment. These emissions, detected at energies approaching twice the ponderomotive energy (2⁢𝑈 p ), indicate a mechanism where electrons are nonadiabatically ejected with substantial inward velocity following ionization. The bump-like features arise from returning trajectories that scatter off the residual ion. We interpret these experimental results classically using a periodically driven version of the Bohr model and quantum mechanically through Keldysh's adiabaticity parameter (𝛾 K ) and Reiss's continuum-state intensity parameter 𝒵 in a Coulomb-corrected version of the strong-field approximation (SFA) that incorporates “complex” electronic trajectories. Notably, our theoretical analysis shows that these emissions are independent of the specific short-range form of the atomic potential.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Reshaping the Quantum Arrow of Time

While the microscopic laws of physics are often symmetric under time reversal, most natural processes that we observe are not. The emergent asymmetry between typical and time-reversed processes is referred to as the arrow of time. In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system. We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward. The control tools are based on the explicit construction of a Hamiltonian that can replicate the stochastic trajectories of a monitored quantum system. Such a Hamiltonian can reverse the effect of monitoring and, via a feedback process, generate trajectories consistent with a reversed arrow of time. It can also be used to simulate the backward-in-time dynamics of an open quantum system. Finally, we design a feedback-driven continuous measurement engine powered by the energy pumped into the system by the monitoring process. We show that the engine can operate under experimentally realizable conditions with feedback delay and finite-efficiency measurements.

Entropy production↗
DOE OSTI2024

Evolution of Efimov states

The Efimov phenomenon manifests itself as an emergent discrete scaling symmetry in the quantum three-body problem. In the unitarity limit, it leads to an infinite tower of three-body bound states with energies forming a geometric sequence. Here in this work, we study the evolution of these so-called Efimov states using relativistic scattering theory. We identify them as poles of the three-particle S matrix and trace their trajectories in the complex energy plane as they evolve from virtual states through bound states to resonances. We dial the scattering parameters toward the unitarity limit and observe the emergence of the universal scaling of energies and couplings - a behavior known from the non-relativistic case. Interestingly, we find that Efimov resonances follow unusual, cyclic trajectories accumulating at the three-body threshold and then disappear at some values of the two-body scattering length. We propose a partial resolution to this ?missing states? problem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Neural units with time-dependent functionality

We show that the time-resolved dynamics of an underdamped harmonic oscillator can be used to do multifunctional computation, performing distinct computations at distinct times within a single dynamical trajectory. We consider the amplitude of an oscillator whose inputs influence its frequency. The activity of the oscillator at fixed times is a nonmonotonic function of its inputs, so it can solve problems such as XOR that are not linearly separable. The activity of the oscillator at fixed input is a nonmonotonic function of time, so it is multifunctional in a temporal sense, and able to carry out distinct nonlinear computations at distinct times within the same dynamical trajectory. We show that a single oscillator, observed at different times, can act as all of the elementary logic gates and perform binary addition, the latter usually implemented in hardware using five logic gates. We show that a set of n oscillators, observed at different times, can perform an arbitrary number of analog-to-n-bit digital conversions. We also show that oscillators can be trained by gradient descent to perform distinct classification tasks at distinct times. Computing with time-dependent functionality can be done in or out of equilibrium, and suggests a way of reducing the number of parameters or devices required to do nonlinear computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anomalies in the Electronic Stopping of Slow Antiprotons in LiF

We present first-principles theoretical calculations for the electronic stopping power (SP) of both protons and antiprotons in LiF. Our results show the presence of the Barkas effect: a higher stopping for positively charged particles than their negatively charged antiparticles. In contrast, a previous study has predicted an anti-Barkas effect (higher stopping for negative charges) at low velocity [Qi, Bruneval and Maliyov, Phys. Rev. Lett. 128, 043401 (2022)]. Here, we explain this discrepancy by showing that this anti-Barkas effect appears for highly symmetric trajectories and disappears when considering trajectories that better reproduce the experimental setup. Our low-velocity results show that the SP of both protons and antiproton vanish for velocities under 0.1 a.u.

3-dimensional systems↗